A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy

Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and uncovering regulatory relationships. A total of 113 signature genes were selected from more than 20,000 genes. The root mean square error of the model was only 0.0025, which was much lower than previously published results, suggesting that our method can predict radiosensitivity with the highest accuracy. Additionally, our regulatory network analysis identified 24 highly important ‘hub’ genes. The data analysis workflow we propose provides a unified and computational framework to harness the full potential of large-scale integrated cancer genomic data for integrative signature discovery. Furthermore, the regression model, signature genes, and their regulatory network should provide a reliable quantitative reference for optimizing personalized treatment options, and may aid our understanding of cancer progress mechanisms.

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gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit

10.1101/2021.07.13.452214 ◽

2021 ◽

Author(s):

Marouen Ben Guebila ◽

Daniel C Morgan ◽

Kimberly Glass ◽

Marieke Lydia Kuijjer ◽

Dawn L DeMeo ◽

...

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Network ◽

Regulatory Networks ◽

Large Scale ◽

Network Inference ◽

Cost Effective ◽

Gene Regulatory Network Inference ◽

Genomic Studies ◽

On Line ◽

Gene Regulatory

Gene regulatory network inference allows for the study of transcriptional control to identify the alteration of cellular processes in human diseases. Our group has developed several tools to model a variety of regulatory processes, including transcriptional (PANDA, SPIDER) and post-transcriptional (PUMA) gene regulation, and gene regulation in individual samples (LIONESS). These methods work by performing repeated operations on data matrices in order to integrate information across multiple lines of biological evidence. This limits their use for large-scale genomic studies due to the associated high computational burden. To address this limitation, we developed gpuZoo, which includes GPU-accelerated implementations of these algorithms. The runtime of the gpuZoo implementation in MATLAB and Python is up to 61 times faster and 28 times less expensive than the multi-core CPU implementation of the same methods. gpuZoo takes advantage of the modern multi-GPU device architecture to build a population of sample-specific gene regulatory networks with similar runtime and cost improvements by combining GPU acceleration with an efficient on-line derivation. Taken together, gpuZoo allows parallel and on-line gene regulatory network inference in large-scale genomic studies with cost-effective performance. gpuZoo is available in MATLAB through the netZooM package https://github.com/netZoo/netZooM and in Python through the netZooPy package https://github.com/netZoo/netZooPy.

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The Reasonable Effectiveness of Randomness in Scalable and Integrative Gene Regulatory Network Inference and Beyond

Computation ◽

10.3390/computation9120146 ◽

2021 ◽

Vol 9 (12) ◽

pp. 146

Author(s):

Michael Banf ◽

Thomas Hartwig

Keyword(s):

Computational Biology ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Regulatory Networks ◽

Large Scale ◽

Network Inference ◽

Gene Regulatory Network Inference ◽

Vast Number ◽

Gene Regulatory ◽

Randomized Methods

Gene regulation is orchestrated by a vast number of molecules, including transcription factors and co-factors, chromatin regulators, as well as epigenetic mechanisms, and it has been shown that transcriptional misregulation, e.g., caused by mutations in regulatory sequences, is responsible for a plethora of diseases, including cancer, developmental or neurological disorders. As a consequence, decoding the architecture of gene regulatory networks has become one of the most important tasks in modern (computational) biology. However, to advance our understanding of the mechanisms involved in the transcriptional apparatus, we need scalable approaches that can deal with the increasing number of large-scale, high-resolution, biological datasets. In particular, such approaches need to be capable of efficiently integrating and exploiting the biological and technological heterogeneity of such datasets in order to best infer the underlying, highly dynamic regulatory networks, often in the absence of sufficient ground truth data for model training or testing. With respect to scalability, randomized approaches have proven to be a promising alternative to deterministic methods in computational biology. As an example, one of the top performing algorithms in a community challenge on gene regulatory network inference from transcriptomic data is based on a random forest regression model. In this concise survey, we aim to highlight how randomized methods may serve as a highly valuable tool, in particular, with increasing amounts of large-scale, biological experiments and datasets being collected. Given the complexity and interdisciplinary nature of the gene regulatory network inference problem, we hope our survey maybe helpful to both computational and biological scientists. It is our aim to provide a starting point for a dialogue about the concepts, benefits, and caveats of the toolbox of randomized methods, since unravelling the intricate web of highly dynamic, regulatory events will be one fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases.

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Large scale gene regulatory network inference with a multi-level strategy

Molecular BioSystems ◽

10.1039/c5mb00560d ◽

2016 ◽

Vol 12 (2) ◽

pp. 588-597 ◽

Cited By ~ 14

Author(s):

Jun Wu ◽

Xiaodong Zhao ◽

Zongli Lin ◽

Zhifeng Shao

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Large Scale ◽

Network Inference ◽

Biological Processes ◽

Molecular Processes ◽

Gene Regulatory Network Inference ◽

Cell Functions ◽

Multi Level ◽

Gene Regulatory

Transcriptional regulation is a basis of many crucial molecular processes and an accurate inference of the gene regulatory network is a helpful and essential task to understand cell functions and gain insights into biological processes of interest in systems biology.

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Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

10.1101/735514 ◽

2019 ◽

Author(s):

Daniel Morgan ◽

Matthew Studham ◽

Andreas Tjärnberg ◽

Holger Weishaupt ◽

Fredrik J. Swartling ◽

...

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Regulatory Networks ◽

Network Inference ◽

Gene Regulatory Network Inference ◽

Validation Data ◽

Regulatory Interactions ◽

Link Type ◽

Gene Regulatory ◽

Novel Interactions

AbstractThe gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. Reliable inference of GRNs is however still a major challenge in systems biology.To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes under a different perturbation design. It agrees with many known links, in addition to predicting a large number of novel interactions from which a subset was experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.Data available at GSE125958Inferred GRNs and inference statistics available at https://dcolin.shinyapps.io/CancerGRN/ Software available at https://bitbucket.org/sonnhammergrni/genespider/src/BFECV/Author SummaryCancer is the second most common cause of death globally, and although cancer treatments have improved in recent years, we need to understand how regulatory mechanisms are altered in cancer to combat the disease efficiently. By applying gene perturbations and inference of gene regulatory networks to 40 genes known or suspected to have a role in cancer due to interactions with the oncogene MYC, we deduce their underlying regulatory interactions. Using a recent computational framework for inference together with a novel method for cross validation, we infer a reliable regulatory model of this system in a completely data driven manner, not reliant on literature or priors. The novel interactions add to the understanding of the progressive oncogenic regulatory process and may provide new targets for therapy.

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Inferring Gene Regulatory Networks from Genetical Genomics Data

Handbook of Research on Computational Methodologies in Gene Regulatory Networks ◽

10.4018/978-1-60566-685-3.ch004 ◽

2010 ◽

pp. 79-107 ◽

Cited By ~ 2

Author(s):

Bing Liu ◽

Ina Hoeschele ◽

Alberto de la Fuente

Keyword(s):

Gene Expression ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Regulatory Networks ◽

Network Inference ◽

Dna Marker ◽

Search Space ◽

Genetical Genomics ◽

Gene Regulatory Network Inference ◽

Gene Regulatory

In this chapter, we review the current state of Gene Regulatory Network inference based on ‘Genetical Genomics’ experiments (Brem & Kruglyak, 2005; Brem, Yvert, Clinton & Kruglyak, 2002; Jansen, 2003; Jansen & Nap, 2001; Schadt et al., 2003) as a special case of causal network inference in ‘Systems Genetics’ (Threadgill, 2006). In a Genetical Genomics experiment, a segregating or genetically randomized population is DNA marker genotyped and gene-expression profiled on a genomewide scale. The genotypes are regarded as natural, multifactorial perturbations resulting in different gene-expression ‘phenotypes’, and causal relationships can therefore be established between the measured genotypes and the gene-expression phenotypes. In this chapter, we review different computational approaches to Gene Regulatory Network inference based on the joint analysis of DNA marker and expression data and additionally of DNA sequence information if available. This includes different methods for expression QTL mapping, selection of regulator-target pairs, construction of an encompassing network, which strongly constrains the network search space, and pairwise and multivariate methods for Gene Regulatory Network inference, such as Bayesian Networks and Structural Equation Modeling.

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A novel probabilistic generator for large-scale gene association networks

PLoS ONE ◽

10.1371/journal.pone.0259193 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0259193

Author(s):

Tyler Grimes ◽

Somnath Datta

Keyword(s):

Gold Standard ◽

Regulatory Network ◽

Regulatory Networks ◽

Large Scale ◽

Network Inference ◽

Real Data ◽

R Package ◽

Reference Network ◽

Gene Association ◽

Inference Methods

Motivation Gene expression data provide an opportunity for reverse-engineering gene-gene associations using network inference methods. However, it is difficult to assess the performance of these methods because the true underlying network is unknown in real data. Current benchmarks address this problem by subsampling a known regulatory network to conduct simulations. But the topology of regulatory networks can vary greatly across organisms or tissues, and reference-based generators—such as GeneNetWeaver—are not designed to capture this heterogeneity. This means, for example, benchmark results from the E. coli regulatory network will not carry over to other organisms or tissues. In contrast, probabilistic generators do not require a reference network, and they have the potential to capture a rich distribution of topologies. This makes probabilistic generators an ideal approach for obtaining a robust benchmarking of network inference methods. Results We propose a novel probabilistic network generator that (1) provides an alternative to address the inherent limitation of reference-based generators and (2) is able to create realistic gene association networks, and (3) captures the heterogeneity found across gold-standard networks better than existing generators used in practice. Eight organism-specific and 12 human tissue-specific gold-standard association networks are considered. Several measures of global topology are used to determine the similarity of generated networks to the gold-standards. Along with demonstrating the variability of network structure across organisms and tissues, we show that the commonly used “scale-free” model is insufficient for replicating these structures. Availability This generator is implemented in the R package “SeqNet” and is available on CRAN (https://cran.r-project.org/web/packages/SeqNet/index.html).

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Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis

Bioinformatics ◽

10.1093/bioinformatics/btr454 ◽

2011 ◽

Vol 27 (19) ◽

pp. 2686-2691 ◽

Cited By ~ 59

Author(s):

Zheng Li ◽

Ping Li ◽

Arun Krishnan ◽

Jingdong Liu

Keyword(s):

Differential Equation ◽

Network Analysis ◽

Regulatory Network ◽

Large Scale ◽

Network Inference ◽

Dynamic Bayesian Network ◽

Local Dynamic ◽

Gene Regulatory Network Inference ◽

Differential Equation Models ◽

Gene Regulatory

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Overview of Gene Regulatory Network Inference Based on Differential Equation Models

Current Protein and Peptide Science ◽

10.2174/1389203721666200213103350 ◽

2020 ◽

Vol 21 (11) ◽

pp. 1054-1059

Author(s):

Bin Yang ◽

Yuehui Chen

Keyword(s):

Differential Equation ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Network Inference ◽

New Drugs ◽

Gene Regulatory Network Inference ◽

Ode Models ◽

Differential Equation Models ◽

Linear Ode ◽

Gene Regulatory

: Reconstruction of gene regulatory networks (GRN) plays an important role in understanding the complexity, functionality and pathways of biological systems, which could support the design of new drugs for diseases. Because differential equation models are flexible androbust, these models have been utilized to identify biochemical reactions and gene regulatory networks. This paper investigates the differential equation models for reverse engineering gene regulatory networks. We introduce three kinds of differential equation models, including ordinary differential equation (ODE), time-delayed differential equation (TDDE) and stochastic differential equation (SDE). ODE models include linear ODE, nonlinear ODE and S-system model. We also discuss the evolutionary algorithms, which are utilized to search the optimal structures and parameters of differential equation models. This investigation could provide a comprehensive understanding of differential equation models, and lead to the discovery of novel differential equation models.

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